发表机构
Massachusetts Institute of Technology; Lawrence Berkeley National Lab（麻省理工学院; 劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出利用新颖不变晶格表示法从粉末XRD数据确定晶胞参数的机器学习方法，基于双谱构建倒易晶格不变表示，计算可微且用动态规划求逆，相比直接预测更准确，还与现有模型对比评估，其不变表示有望用于更多晶体学机器学习任务。
AI 中文摘要
我们提出了一种机器学习方法，利用一种新颖的不变晶格表示法从粉末X射线衍射（XRD）数据中确定晶胞参数。机器学习中，数据表示对预测质量有重大影响。以往方法直接从XRD输入预测晶格参数，但这些参数强烈依赖晶胞约化或惯例。本文基于双谱构建了与原胞惯例无关的倒易晶格不变表示，双谱计算可微，用动态规划方法求逆。结果表明，固定模型架构时，用晶格双谱作目标比直接用晶胞参数预测更准确，如在MP - 20数据集上，双谱降低了长度平均绝对百分比误差和角度平均绝对百分比误差。此外还与现有模型对比并在RRUFF数据集上评估。这种不变晶格表示有望用于更多晶体学机器学习任务。
英文摘要
We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data representation used can have a substantial impact on the prediction quality. Previous approaches have directly predicted lattice parameters ($a,b,c,α,β,γ$) from XRD inputs. However, these parameters depend strongly on the unit cell reduction or convention used. In this work, we construct an invariant representation of the reciprocal lattice that is independent of primitive cell convention, based on the bispectrum--a descriptor built from spherical harmonic projections of lattice points. The calculation of the lattice bispectrum is differentiable, and we demonstrate how to invert it using gradient-based optimization initialized from a nearest-neighbor lookup. We show that with a fixed ML model architecture, using the lattice bispectrum as the ML target rather than the unit cell parameters leads to more accurate lattice parameter predictions. For example, using the MP-20 dataset, the bispectrum reduces length mean absolute percentage error (MAPE) from 11.08% to 2.43% and angle MAPE from 12.15% to 2.45% compared to direct prediction with the same model architecture. We additionally benchmark our approach against pre-existing XRD to crystal structure models such as Crystalyze and assess its performance on experimental data. Beyond unit cell representation, we anticipate this invariant lattice representation could serve more broadly as a geometry-aware target for other crystallographic machine learning tasks such as structure generation.
CommentsAccepted, Acta Crystallographica Section A
